Inthispaper,wepresentanewapproachtodiagnosisinstudentmodelingbasedonthe use of Bayesian Networks and Computer Adaptive Tests. A new integrated Bayesian student model is de¢ned and then combined with an Adaptive Testing algorithm. The structural model de¢ned has the advantage that it measures students abilities at different levels of granularity, allows substantial simpli¢cations when specifying the parameters (conditional probabilities) needed to construct the Bayesian Network that describes the student model, and supports the Adaptive Diagnosis algorithm. The validity of the approach has been tested intensively by using simulated students.The results obtained show that the Bayesian student model has excellent performance in terms of accuracy, and that the introduction of adaptive question selection methods improves its behavior both in terms of accuracy and ef¢ciency.
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